TaxoGrasp: Taxonomy-Guided Human Grasp Synthesis with Sparse Contact Constraint

We tackle controllable human grasp synthesis under sparse user-specified thumb-contact constraints. While existing methods use contact as conditioning, they lack explicit semantic grounding, yielding weak contact-intent coupling, poor constraint sensitivity, and pose drift. We propose TaxoGrasp, a framework threading Feix grasp taxonomy as a persistent intent bottleneck throughout: (1) intent inference via supervised taxonomy prediction from object geometry and thumb-contact constraint, (2) diffusion synthesis conditioned on taxonomy templates, and (3) anchor-optimized grounding preserving taxonomy structure. We augment HOGraspNet with patch-level thumb-contact annotations and introduce a standardized evaluation protocol with three metric categories. Experiments demonstrate improved intent consistency, physical plausibility, and constraint adherence.